APPLICANT'S ABSTRACT: The focus of this study is on testing techniques that facilitate the identification of youth at-high risk for drug involvement, school disengagement, and suicide risk behaviors. The proposal addresses a fundamental and pragmatic need to identify accurately high-risk youth for prevention efforts without creating costs of additional data gathering or complicated procedures for screening youth. Developing and testing simple models for accurately and efficiently finding high-risk youth among school populations is a neglected area of study; and yet, this is a critical first step in any preventive intervention research. The research aims are to (1)test an existing high-risk youth selection model currently used to identify youth at-high risk of school failure, and thereby, drug involvement; (2)evaluate alternative models of selection which maximize efficiency of data collection and accuracy of classification; (3)explore the ability of the models to screen for degree of risk; and (4)to test models of selection for specific population subgroups: a)male/female, b)ethnic minorities and c)age-based subgroups (aged 14-15 & 16-18 years). An existing data base will be used for this study; i.e., previously collected data from (five) years of school district records and individual student surveys which contain basic information on drug involvement, school performance, and suicide risk behaviors. Measures of these behaviors have high reliability and validity and form the basis for classifying the student population. The research design calls for splitting the data into subsamples, using part of the data to derive the selection models (primarily based on Classification and Regression Tree, CART, procedures) and, then, using the remaining samples to test the accuracy of the derived models. This design is analagous to typical split-half designs. The CART procedures are specifically designed to (1)find a limited number of classifiers from a larger set of variables and (2)minimize error rates in classification. Variables that were systematically gathered in the school district files will be used as the independent factors or classifiers of the individual cases; these classifiers stand as proxies for well-known risk factors. The expressed interest is to work within an extant dataset typically and systematically available in a school setting to produce a model that accurately selects youth of high-risk status. The value of this study is to test and to identify a simple means for classifying risk status in a typical site for preventive intervention programs, the schools. If such a procedure is successful, the high-risk youth sample selection model will provide a building block in future studies, providing a simple and pragmatic means for : (1) assessing prevalence of risk in school populations; (2) linking high risk youth to interventions and (3) implementing intervention programs across school settings.